{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "0b41c625",
   "metadata": {},
   "source": [
    "# Probabilistic Forecasting: Conformal Prediction"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c0137385",
   "metadata": {},
   "source": [
    "Conformal prediction is a framework for constructing prediction intervals that are guaranteed to contain the true value with a specified probability (coverage probability). It works by combining the predictions of a point-forecasting model with its past residuals—differences between previous predictions and actual values. These residuals help estimate the uncertainty in the forecast and determine the width of the prediction interval that is then added to the point forecast. Skforecast implements [Split Conformal Prediction (SCP)](https://mapie.readthedocs.io/en/stable/theoretical_description_regression.html#the-split-method).\n",
    "\n",
    "<p style=\"text-align: center\">\n",
    "    <img src=\"../img/conformal-regression.png\" style=\"width: 450px\">\n",
    "    <br>\n",
    "    <font size=\"2.5\"> <i>Conformal regression turns point predictions into prediction intervals. Source: Introduction To Conformal Prediction With Python: A Short Guide For Quantifying Uncertainty Of Machine Learning Models\n",
    "by Christoph Molnar https://leanpub.com/conformal-prediction</i></font>\n",
    "</p>\n",
    "\n",
    "Conformal methods can also calibrate prediction intervals generated by other techniques, such as quantile regression or bootstrapped residuals. In this case, the conformal method adjusts the prediction intervals to ensure that they remain valid with respect to the coverage probability. Skforecast provides this functionality through the `ConformalIntervalCalibrator` transformer."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e211c005",
   "metadata": {},
   "source": [
    "<div class=\"admonition note\" name=\"html-admonition\" style=\"background: rgba(255,145,0,.1); padding-top: 0px; padding-bottom: 6px; border-radius: 8px; border-left: 8px solid #ff9100; border-color: #ff9100; padding-left: 10px; padding-right: 10px\">\n",
    "\n",
    "<p class=\"title\">\n",
    "    <i style=\"font-size: 18px; color:#ff9100; border-color: #ff1744;\"></i>\n",
    "    <b style=\"color: #ff9100;\"> <span style=\"color: #ff9100;\">&#9888;</span> Warning</b>\n",
    "</p>\n",
    "\n",
    "There are several well-established <a href=\"https://mapie.readthedocs.io/en/stable/theoretical_description_regression.html\" target=\"_blank\">methods for conformal prediction</a>, each with its own characteristics and assumptions. However, when applied to time series forecasting, their coverage guarantees are only valid for one-step-ahead predictions. For multi-step-ahead predictions, the coverage probability is not guaranteed. Skforecast implements Split Conformal Prediction (SCP) due to its simplicity and efficiency. \n",
    "\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d2f26236",
   "metadata": {},
   "source": [
    "<div class=\"admonition note\" name=\"html-admonition\" style=\"background: rgba(0,191,191,.1); padding-top: 0px; padding-bottom: 6px; border-radius: 8px; border-left: 8px solid #00bfa5; border-color: #00bfa5; padding-left: 10px; padding-right: 10px;\">\n",
    "\n",
    "<p class=\"title\">\n",
    "    <i style=\"font-size: 18px; color:#00bfa5;\"></i>\n",
    "    <b style=\"color: #00bfa5;\">&#128161 Tip</b>\n",
    "</p>\n",
    "\n",
    "<p>For more examples on how to use probabilistic forecasting, check out the following articles:</p>\n",
    "<ul>\n",
    "    <li>\n",
    "        <a href=\"https://cienciadedatos.net/documentos/py42-probabilistic-forecasting\" target=\"_blank\">\n",
    "            Probabilistic forecasting with machine learning\n",
    "        </a>\n",
    "    </li>\n",
    "    <li>\n",
    "        <a href=\"https://cienciadedatos.net/documentos/py60-probabilistic-forecasting-prediction-intervals-multi-step-forecasting\" target=\"_blank\">\n",
    "            Probabilistic forecasting: prediction intervals for multi-step time series forecasting\n",
    "        </a>\n",
    "    </li>\n",
    "    <li>\n",
    "        <a href=\"../faq/probabilistic-forecasting-crps-score.html\" target=\"_blank\">\n",
    "            Continuous Ranked Probability Score (CRPS) in probabilistic forecasting\n",
    "        </a>\n",
    "    </li>\n",
    "</ul>\n",
    "\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fcdaabcf",
   "metadata": {},
   "source": [
    "## Libraries and data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "7eb1f412",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Data processing\n",
    "# ==============================================================================\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from skforecast.datasets import fetch_dataset\n",
    "\n",
    "# Plots\n",
    "# ==============================================================================\n",
    "import matplotlib.pyplot as plt\n",
    "from skforecast.plot import set_dark_theme, plot_residuals, plot_prediction_intervals\n",
    "\n",
    "# Modelling and Forecasting\n",
    "# ==============================================================================\n",
    "from lightgbm import LGBMRegressor\n",
    "from skforecast.recursive import ForecasterRecursive\n",
    "from skforecast.preprocessing import RollingFeatures\n",
    "from skforecast.model_selection import TimeSeriesFold, backtesting_forecaster\n",
    "from skforecast.metrics import calculate_coverage\n",
    "\n",
    "# Configuration\n",
    "# ==============================================================================\n",
    "import warnings\n",
    "warnings.filterwarnings('once')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "4dba164b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">╭──────────────────────────────── <span style=\"font-weight: bold\">ett_m2_extended</span> ─────────────────────────────────╮\n",
       "│ <span style=\"font-weight: bold\">Description:</span>                                                                     │\n",
       "│ Data from an electricity transformer station was collected between July 2016 and │\n",
       "│ July 2018 (2 years x 365 days x 24 hours x 4 intervals per hour = 70,080 data    │\n",
       "│ points). Each data point consists of 8 features, including the date of the       │\n",
       "│ point, the predictive value \"Oil Temperature (OT)\", and 6 different types of     │\n",
       "│ external power load features: High UseFul Load (HUFL), High UseLess Load (HULL), │\n",
       "│ Middle UseFul Load (MUFL), Middle UseLess Load (MULL), Low UseFul Load (LUFL),   │\n",
       "│ Low UseLess Load (LULL). Additional variables are created based on calendar      │\n",
       "│ information (year, month, week, day of the week, and hour). These variables have │\n",
       "│ been encoded using the cyclical encoding technique (sin and cos transformations) │\n",
       "│ to preserve the cyclical nature of the data.                                     │\n",
       "│                                                                                  │\n",
       "│ <span style=\"font-weight: bold\">Source:</span>                                                                          │\n",
       "│ Zhou, Haoyi &amp; Zhang, Shanghang &amp; Peng, Jieqi &amp; Zhang, Shuai &amp; Li, Jianxin &amp;      │\n",
       "│ Xiong, Hui &amp; Zhang, Wancai. (2020). Informer: Beyond Efficient Transformer for   │\n",
       "│ Long Sequence Time-Series Forecasting.                                           │\n",
       "│ [10.48550/arXiv.2012.07436](https://arxiv.org/abs/2012.07436).                   │\n",
       "│ https://github.com/zhouhaoyi/ETDataset                                           │\n",
       "│                                                                                  │\n",
       "│ <span style=\"font-weight: bold\">URL:</span>                                                                             │\n",
       "│ https://raw.githubusercontent.com/skforecast/skforecast-                         │\n",
       "│ datasets/main/data/ETTm2_extended.csv                                            │\n",
       "│                                                                                  │\n",
       "│ <span style=\"font-weight: bold\">Shape:</span> 69680 rows x 16 columns                                                   │\n",
       "╰──────────────────────────────────────────────────────────────────────────────────╯\n",
       "</pre>\n"
      ],
      "text/plain": [
       "╭──────────────────────────────── \u001b[1mett_m2_extended\u001b[0m ─────────────────────────────────╮\n",
       "│ \u001b[1mDescription:\u001b[0m                                                                     │\n",
       "│ Data from an electricity transformer station was collected between July 2016 and │\n",
       "│ July 2018 (2 years x 365 days x 24 hours x 4 intervals per hour = 70,080 data    │\n",
       "│ points). Each data point consists of 8 features, including the date of the       │\n",
       "│ point, the predictive value \"Oil Temperature (OT)\", and 6 different types of     │\n",
       "│ external power load features: High UseFul Load (HUFL), High UseLess Load (HULL), │\n",
       "│ Middle UseFul Load (MUFL), Middle UseLess Load (MULL), Low UseFul Load (LUFL),   │\n",
       "│ Low UseLess Load (LULL). Additional variables are created based on calendar      │\n",
       "│ information (year, month, week, day of the week, and hour). These variables have │\n",
       "│ been encoded using the cyclical encoding technique (sin and cos transformations) │\n",
       "│ to preserve the cyclical nature of the data.                                     │\n",
       "│                                                                                  │\n",
       "│ \u001b[1mSource:\u001b[0m                                                                          │\n",
       "│ Zhou, Haoyi & Zhang, Shanghang & Peng, Jieqi & Zhang, Shuai & Li, Jianxin &      │\n",
       "│ Xiong, Hui & Zhang, Wancai. (2020). Informer: Beyond Efficient Transformer for   │\n",
       "│ Long Sequence Time-Series Forecasting.                                           │\n",
       "│ [10.48550/arXiv.2012.07436](https://arxiv.org/abs/2012.07436).                   │\n",
       "│ https://github.com/zhouhaoyi/ETDataset                                           │\n",
       "│                                                                                  │\n",
       "│ \u001b[1mURL:\u001b[0m                                                                             │\n",
       "│ https://raw.githubusercontent.com/skforecast/skforecast-                         │\n",
       "│ datasets/main/data/ETTm2_extended.csv                                            │\n",
       "│                                                                                  │\n",
       "│ \u001b[1mShape:\u001b[0m 69680 rows x 16 columns                                                   │\n",
       "╰──────────────────────────────────────────────────────────────────────────────────╯\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>OT</th>\n",
       "      <th>day_of_week_cos</th>\n",
       "      <th>day_of_week_sin</th>\n",
       "      <th>hour_cos</th>\n",
       "      <th>hour_sin</th>\n",
       "      <th>month_cos</th>\n",
       "      <th>month_sin</th>\n",
       "      <th>week_cos</th>\n",
       "      <th>week_sin</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-07-01 00:00:00</th>\n",
       "      <td>38.661999</td>\n",
       "      <td>-0.900969</td>\n",
       "      <td>-0.433884</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.866025</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>1.224647e-16</td>\n",
       "      <td>2016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-07-01 00:15:00</th>\n",
       "      <td>38.223000</td>\n",
       "      <td>-0.900969</td>\n",
       "      <td>-0.433884</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.866025</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>1.224647e-16</td>\n",
       "      <td>2016</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            OT  day_of_week_cos  day_of_week_sin  hour_cos  \\\n",
       "date                                                                         \n",
       "2016-07-01 00:00:00  38.661999        -0.900969        -0.433884       1.0   \n",
       "2016-07-01 00:15:00  38.223000        -0.900969        -0.433884       1.0   \n",
       "\n",
       "                     hour_sin  month_cos  month_sin  week_cos      week_sin  \\\n",
       "date                                                                          \n",
       "2016-07-01 00:00:00       0.0  -0.866025       -0.5      -1.0  1.224647e-16   \n",
       "2016-07-01 00:15:00       0.0  -0.866025       -0.5      -1.0  1.224647e-16   \n",
       "\n",
       "                     year  \n",
       "date                       \n",
       "2016-07-01 00:00:00  2016  \n",
       "2016-07-01 00:15:00  2016  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Data\n",
    "# ==============================================================================\n",
    "data = fetch_dataset(name=\"ett_m2_extended\")\n",
    "data = data[[\n",
    "    \"OT\",\n",
    "    \"day_of_week_cos\",\n",
    "    \"day_of_week_sin\",\n",
    "    \"hour_cos\",\n",
    "    \"hour_sin\",\n",
    "    \"month_cos\",\n",
    "    \"month_sin\",\n",
    "    \"week_cos\",\n",
    "    \"week_sin\",\n",
    "    \"year\",\n",
    "]]\n",
    "data.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "df646c40",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dates train      : 2016-07-01 00:00:00 --- 2017-10-01 23:45:00  (n=43968)\n",
      "Dates calibration: 2017-10-02 00:00:00 --- 2018-04-01 23:45:00  (n=17472)\n",
      "Dates test       : 2018-04-02 00:00:00 --- 2018-06-26 19:45:00  (n=8240)\n"
     ]
    }
   ],
   "source": [
    "# Split train-calibration-test\n",
    "# ==============================================================================\n",
    "exog_features = data.columns.difference(['OT']).tolist()\n",
    "end_train = '2017-10-01 23:59:00'\n",
    "end_calibration = '2018-04-01 23:59:00'\n",
    "data_train = data.loc[: end_train, :]\n",
    "data_cal   = data.loc[end_train:end_calibration, :]\n",
    "data_test  = data.loc[end_calibration:, :]\n",
    "\n",
    "print(f\"Dates train      : {data_train.index.min()} --- {data_train.index.max()}  (n={len(data_train)})\")\n",
    "print(f\"Dates calibration: {data_cal.index.min()} --- {data_cal.index.max()}  (n={len(data_cal)})\")\n",
    "print(f\"Dates test       : {data_test.index.min()} --- {data_test.index.max()}  (n={len(data_test)})\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "f88ab48b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot partitions\n",
    "# ==============================================================================\n",
    "set_dark_theme()\n",
    "plt.rcParams['lines.linewidth'] = 0.5\n",
    "fig, ax = plt.subplots(figsize=(8, 3))\n",
    "ax.plot(data_train['OT'], label='Train')\n",
    "ax.plot(data_cal['OT'], label='Calibration')\n",
    "ax.plot(data_test['OT'], label='Test')\n",
    "ax.set_title('Oil Temperature')\n",
    "ax.legend();"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d6b591db",
   "metadata": {},
   "source": [
    "## Calibration residuals\n",
    "\n",
    "To address the issue of overoptimistic intervals, it is recomended to use out-sample residuals. These are residuals from a calibration set, which contains data not seen during training. These residuals can be obtained through backtesting."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "7e6f3966",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "    <style>\n",
       "        .container-aecb229ca2174887a2ed772c0b28c1ad {\n",
       "            font-family: 'Arial', sans-serif;\n",
       "            font-size: 0.9em;\n",
       "            color: #333333;\n",
       "            border: 1px solid #ddd;\n",
       "            background-color: #f0f8ff;\n",
       "            padding: 5px 15px;\n",
       "            border-radius: 8px;\n",
       "            max-width: 600px;\n",
       "            #margin: auto;\n",
       "        }\n",
       "        .container-aecb229ca2174887a2ed772c0b28c1ad h2 {\n",
       "            font-size: 1.5em;\n",
       "            color: #222222;\n",
       "            border-bottom: 2px solid #ddd;\n",
       "            padding-bottom: 5px;\n",
       "            margin-bottom: 15px;\n",
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       "        }\n",
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       "            color: #001633;\n",
       "            text-decoration: none;\n",
       "        }\n",
       "        .container-aecb229ca2174887a2ed772c0b28c1ad a:hover {\n",
       "            color: #359ccb; \n",
       "        }\n",
       "    </style>\n",
       "    \n",
       "        <div class=\"container-aecb229ca2174887a2ed772c0b28c1ad\">\n",
       "            <p style=\"font-size: 1.5em; font-weight: bold; margin-block-start: 0.83em; margin-block-end: 0.83em;\">ForecasterRecursive</p>\n",
       "            <details open>\n",
       "                <summary>General Information</summary>\n",
       "                <ul>\n",
       "                    <li><strong>Estimator:</strong> LGBMRegressor</li>\n",
       "                    <li><strong>Lags:</strong> [ 1  2  3 23 24 25 47 48 49 71 72 73]</li>\n",
       "                    <li><strong>Window features:</strong> ['roll_mean_72']</li>\n",
       "                    <li><strong>Window size:</strong> 73</li>\n",
       "                    <li><strong>Series name:</strong> OT</li>\n",
       "                    <li><strong>Exogenous included:</strong> True</li>\n",
       "                    <li><strong>Weight function included:</strong> False</li>\n",
       "                    <li><strong>Differentiation order:</strong> None</li>\n",
       "                    <li><strong>Creation date:</strong> 2025-11-26 17:20:08</li>\n",
       "                    <li><strong>Last fit date:</strong> 2025-11-26 17:20:09</li>\n",
       "                    <li><strong>Skforecast version:</strong> 0.19.0</li>\n",
       "                    <li><strong>Python version:</strong> 3.12.11</li>\n",
       "                    <li><strong>Forecaster id:</strong> None</li>\n",
       "                </ul>\n",
       "            </details>\n",
       "            <details>\n",
       "                <summary>Exogenous Variables</summary>\n",
       "                <ul>\n",
       "                    day_of_week_cos, day_of_week_sin, hour_cos, hour_sin, month_cos, month_sin, week_cos, week_sin, year\n",
       "                </ul>\n",
       "            </details>\n",
       "            <details>\n",
       "                <summary>Data Transformations</summary>\n",
       "                <ul>\n",
       "                    <li><strong>Transformer for y:</strong> None</li>\n",
       "                    <li><strong>Transformer for exog:</strong> None</li>\n",
       "                </ul>\n",
       "            </details>\n",
       "            <details>\n",
       "                <summary>Training Information</summary>\n",
       "                <ul>\n",
       "                    <li><strong>Training range:</strong> [Timestamp('2016-07-01 00:00:00'), Timestamp('2018-04-01 23:45:00')]</li>\n",
       "                    <li><strong>Training index type:</strong> DatetimeIndex</li>\n",
       "                    <li><strong>Training index frequency:</strong> <15 * Minutes></li>\n",
       "                </ul>\n",
       "            </details>\n",
       "            <details>\n",
       "                <summary>Estimator Parameters</summary>\n",
       "                <ul>\n",
       "                    {'boosting_type': 'gbdt', 'class_weight': None, 'colsample_bytree': 1.0, 'importance_type': 'split', 'learning_rate': 0.1, 'max_depth': 4, 'min_child_samples': 20, 'min_child_weight': 0.001, 'min_split_gain': 0.0, 'n_estimators': 100, 'n_jobs': None, 'num_leaves': 31, 'objective': None, 'random_state': 15926, 'reg_alpha': 0.0, 'reg_lambda': 0.0, 'subsample': 1.0, 'subsample_for_bin': 200000, 'subsample_freq': 0, 'verbose': -1}\n",
       "                </ul>\n",
       "            </details>\n",
       "            <details>\n",
       "                <summary>Fit Kwargs</summary>\n",
       "                <ul>\n",
       "                    {}\n",
       "                </ul>\n",
       "            </details>\n",
       "            <p>\n",
       "                <a href=\"https://skforecast.org/0.19.0/api/forecasterrecursive.html\">&#128712 <strong>API Reference</strong></a>\n",
       "                &nbsp;&nbsp;\n",
       "                <a href=\"https://skforecast.org/0.19.0/user_guides/autoregressive-forecaster.html\">&#128462 <strong>User Guide</strong></a>\n",
       "            </p>\n",
       "        </div>\n",
       "        "
      ],
      "text/plain": [
       "=================== \n",
       "ForecasterRecursive \n",
       "=================== \n",
       "Estimator: LGBMRegressor \n",
       "Lags: [ 1  2  3 23 24 25 47 48 49 71 72 73] \n",
       "Window features: ['roll_mean_72'] \n",
       "Window size: 73 \n",
       "Series name: OT \n",
       "Exogenous included: True \n",
       "Exogenous names: \n",
       "    day_of_week_cos, day_of_week_sin, hour_cos, hour_sin, month_cos, month_sin,\n",
       "    week_cos, week_sin, year \n",
       "Transformer for y: None \n",
       "Transformer for exog: None \n",
       "Weight function included: False \n",
       "Differentiation order: None \n",
       "Training range: [Timestamp('2016-07-01 00:00:00'), Timestamp('2018-04-01 23:45:00')] \n",
       "Training index type: DatetimeIndex \n",
       "Training index frequency: <15 * Minutes> \n",
       "Estimator parameters: \n",
       "    {'boosting_type': 'gbdt', 'class_weight': None, 'colsample_bytree': 1.0,\n",
       "    'importance_type': 'split', 'learning_rate': 0.1, 'max_depth': 4,\n",
       "    'min_child_samples': 20, 'min_child_weight': 0.001, 'min_split_gain': 0.0,\n",
       "    'n_estimators': 100, 'n_jobs': None, 'num_leaves': 31, 'objective': None,\n",
       "    'random_state': 15926, 'reg_alpha': 0.0, 'reg_lambda': 0.0, 'subsample':\n",
       "    1.0, 'subsample_for_bin': 200000, 'subsample_freq': 0, 'verbose': -1} \n",
       "fit_kwargs: {} \n",
       "Creation date: 2025-11-26 17:20:08 \n",
       "Last fit date: 2025-11-26 17:20:09 \n",
       "Skforecast version: 0.19.0 \n",
       "Python version: 3.12.11 \n",
       "Forecaster id: None "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create and fit forecaster\n",
    "# ==============================================================================\n",
    "params = {\n",
    "    \"max_depth\": 4,\n",
    "    \"verbose\": -1,\n",
    "    \"random_state\": 15926\n",
    "}\n",
    "lags = [1, 2, 3, 23, 24, 25, 47, 48, 49, 71, 72, 73]\n",
    "window_features = RollingFeatures(stats=[\"mean\"], window_sizes=24 * 3)\n",
    "\n",
    "forecaster = ForecasterRecursive(\n",
    "                 estimator       = LGBMRegressor(**params),\n",
    "                 lags            = lags,\n",
    "                 window_features = window_features,\n",
    "             )\n",
    "\n",
    "forecaster.fit(\n",
    "    y    = data.loc[:end_calibration, 'OT'],\n",
    "    exog = data.loc[:end_calibration, exog_features]\n",
    ")\n",
    "forecaster"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "beb199f5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a13324ac5d3a4a75864639a556071117",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/728 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Backtesting on calibration data to obtain out-sample residuals\n",
    "# ==============================================================================\n",
    "cv = TimeSeriesFold(\n",
    "         steps              = 24, \n",
    "         initial_train_size = len(data.loc[:end_train]),\n",
    "         refit              = False\n",
    "     )\n",
    "\n",
    "_, predictions_cal = backtesting_forecaster(\n",
    "                         forecaster    = forecaster,\n",
    "                         y             = data.loc[:end_calibration, 'OT'],\n",
    "                         exog          = data.loc[:end_calibration, exog_features],\n",
    "                         cv            = cv,\n",
    "                         metric        = 'mean_absolute_error',\n",
    "                         n_jobs        = 'auto',\n",
    "                         verbose       = False,\n",
    "                         show_progress = True\n",
    "                     )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "b67770c2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "negative    12251\n",
      "positive     5221\n",
      "Name: count, dtype: int64\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x400 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Distribution of out-sample residuals\n",
    "# ==============================================================================\n",
    "residuals = data.loc[predictions_cal.index, 'OT'] - predictions_cal['pred']\n",
    "print(pd.Series(np.where(residuals < 0, 'negative', 'positive')).value_counts())\n",
    "plt.rcParams.update({'font.size': 8})\n",
    "_ = plot_residuals(residuals=residuals, figsize=(7, 4))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c6c00255",
   "metadata": {},
   "source": [
    "With the `set_out_sample_residuals()` method, the out-sample residuals are stored in the forecaster object so that they can be used to calibrate the prediction intervals."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "aa952ce7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Store out-sample residuals in the forecaster\n",
    "# ==============================================================================\n",
    "forecaster.set_out_sample_residuals(\n",
    "    y_true = data.loc[predictions_cal.index, 'OT'], \n",
    "    y_pred = predictions_cal['pred']\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "21ae2470",
   "metadata": {},
   "source": [
    "Now that the new residuals have been added to the forecaster, the prediction intervals can be calculated using `use_in_sample_residuals = False`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "d49ea06d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pred</th>\n",
       "      <th>lower_bound</th>\n",
       "      <th>upper_bound</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-04-02 00:00:00</th>\n",
       "      <td>32.024773</td>\n",
       "      <td>29.387492</td>\n",
       "      <td>34.662053</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-04-02 00:15:00</th>\n",
       "      <td>31.853564</td>\n",
       "      <td>29.216283</td>\n",
       "      <td>34.490845</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-04-02 00:30:00</th>\n",
       "      <td>31.695225</td>\n",
       "      <td>29.057944</td>\n",
       "      <td>34.332506</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-04-02 00:45:00</th>\n",
       "      <td>31.442913</td>\n",
       "      <td>28.805632</td>\n",
       "      <td>34.080194</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-04-02 01:00:00</th>\n",
       "      <td>31.390724</td>\n",
       "      <td>28.753443</td>\n",
       "      <td>34.028005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-04-02 01:15:00</th>\n",
       "      <td>31.137807</td>\n",
       "      <td>28.500526</td>\n",
       "      <td>33.775088</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-04-02 01:30:00</th>\n",
       "      <td>30.926586</td>\n",
       "      <td>28.289305</td>\n",
       "      <td>33.563867</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-04-02 01:45:00</th>\n",
       "      <td>30.793334</td>\n",
       "      <td>28.156053</td>\n",
       "      <td>33.430615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-04-02 02:00:00</th>\n",
       "      <td>30.544763</td>\n",
       "      <td>27.907482</td>\n",
       "      <td>33.182044</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-04-02 02:15:00</th>\n",
       "      <td>30.544763</td>\n",
       "      <td>27.907482</td>\n",
       "      <td>33.182044</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-04-02 02:30:00</th>\n",
       "      <td>30.569183</td>\n",
       "      <td>27.931902</td>\n",
       "      <td>33.206464</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-04-02 02:45:00</th>\n",
       "      <td>30.585077</td>\n",
       "      <td>27.947796</td>\n",
       "      <td>33.222358</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-04-02 03:00:00</th>\n",
       "      <td>30.589153</td>\n",
       "      <td>27.951873</td>\n",
       "      <td>33.226434</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-04-02 03:15:00</th>\n",
       "      <td>30.589153</td>\n",
       "      <td>27.951873</td>\n",
       "      <td>33.226434</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-04-02 03:30:00</th>\n",
       "      <td>30.594971</td>\n",
       "      <td>27.957691</td>\n",
       "      <td>33.232252</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-04-02 03:45:00</th>\n",
       "      <td>30.613403</td>\n",
       "      <td>27.976122</td>\n",
       "      <td>33.250683</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-04-02 04:00:00</th>\n",
       "      <td>30.613403</td>\n",
       "      <td>27.976122</td>\n",
       "      <td>33.250683</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-04-02 04:15:00</th>\n",
       "      <td>30.609986</td>\n",
       "      <td>27.972705</td>\n",
       "      <td>33.247267</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-04-02 04:30:00</th>\n",
       "      <td>30.625439</td>\n",
       "      <td>27.988158</td>\n",
       "      <td>33.262719</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-04-02 04:45:00</th>\n",
       "      <td>30.625439</td>\n",
       "      <td>27.988158</td>\n",
       "      <td>33.262719</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-04-02 05:00:00</th>\n",
       "      <td>30.637124</td>\n",
       "      <td>27.999843</td>\n",
       "      <td>33.274405</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-04-02 05:15:00</th>\n",
       "      <td>30.636440</td>\n",
       "      <td>27.999159</td>\n",
       "      <td>33.273721</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-04-02 05:30:00</th>\n",
       "      <td>30.647419</td>\n",
       "      <td>28.010138</td>\n",
       "      <td>33.284699</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-04-02 05:45:00</th>\n",
       "      <td>30.647419</td>\n",
       "      <td>28.010138</td>\n",
       "      <td>33.284699</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          pred  lower_bound  upper_bound\n",
       "2018-04-02 00:00:00  32.024773    29.387492    34.662053\n",
       "2018-04-02 00:15:00  31.853564    29.216283    34.490845\n",
       "2018-04-02 00:30:00  31.695225    29.057944    34.332506\n",
       "2018-04-02 00:45:00  31.442913    28.805632    34.080194\n",
       "2018-04-02 01:00:00  31.390724    28.753443    34.028005\n",
       "2018-04-02 01:15:00  31.137807    28.500526    33.775088\n",
       "2018-04-02 01:30:00  30.926586    28.289305    33.563867\n",
       "2018-04-02 01:45:00  30.793334    28.156053    33.430615\n",
       "2018-04-02 02:00:00  30.544763    27.907482    33.182044\n",
       "2018-04-02 02:15:00  30.544763    27.907482    33.182044\n",
       "2018-04-02 02:30:00  30.569183    27.931902    33.206464\n",
       "2018-04-02 02:45:00  30.585077    27.947796    33.222358\n",
       "2018-04-02 03:00:00  30.589153    27.951873    33.226434\n",
       "2018-04-02 03:15:00  30.589153    27.951873    33.226434\n",
       "2018-04-02 03:30:00  30.594971    27.957691    33.232252\n",
       "2018-04-02 03:45:00  30.613403    27.976122    33.250683\n",
       "2018-04-02 04:00:00  30.613403    27.976122    33.250683\n",
       "2018-04-02 04:15:00  30.609986    27.972705    33.247267\n",
       "2018-04-02 04:30:00  30.625439    27.988158    33.262719\n",
       "2018-04-02 04:45:00  30.625439    27.988158    33.262719\n",
       "2018-04-02 05:00:00  30.637124    27.999843    33.274405\n",
       "2018-04-02 05:15:00  30.636440    27.999159    33.273721\n",
       "2018-04-02 05:30:00  30.647419    28.010138    33.284699\n",
       "2018-04-02 05:45:00  30.647419    28.010138    33.284699"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Prediction intervals\n",
    "# ==============================================================================\n",
    "forecaster.predict_interval(\n",
    "    steps                   = 24,\n",
    "    exog                    = data.loc[end_calibration:, exog_features],\n",
    "    interval                = [10, 90],\n",
    "    method                  = 'conformal',\n",
    "    use_in_sample_residuals = False,\n",
    "    use_binned_residuals    = True\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "60d9b27c",
   "metadata": {},
   "source": [
    "It is also possible to use forecast prediction intervals within a backtesting loop."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "0830949c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "1d88d08b0f8e4531aca74ead4b84709c",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/344 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fold</th>\n",
       "      <th>pred</th>\n",
       "      <th>lower_bound</th>\n",
       "      <th>upper_bound</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-04-02 00:00:00</th>\n",
       "      <td>0</td>\n",
       "      <td>32.024773</td>\n",
       "      <td>29.387492</td>\n",
       "      <td>34.662053</td>\n",
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       "    <tr>\n",
       "      <th>2018-04-02 00:15:00</th>\n",
       "      <td>0</td>\n",
       "      <td>31.853564</td>\n",
       "      <td>29.216283</td>\n",
       "      <td>34.490845</td>\n",
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       "    <tr>\n",
       "      <th>2018-04-02 00:30:00</th>\n",
       "      <td>0</td>\n",
       "      <td>31.695225</td>\n",
       "      <td>29.057944</td>\n",
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       "    <tr>\n",
       "      <th>2018-04-02 00:45:00</th>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2018-04-02 01:00:00</th>\n",
       "      <td>0</td>\n",
       "      <td>31.390724</td>\n",
       "      <td>28.753443</td>\n",
       "      <td>34.028005</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     fold       pred  lower_bound  upper_bound\n",
       "2018-04-02 00:00:00     0  32.024773    29.387492    34.662053\n",
       "2018-04-02 00:15:00     0  31.853564    29.216283    34.490845\n",
       "2018-04-02 00:30:00     0  31.695225    29.057944    34.332506\n",
       "2018-04-02 00:45:00     0  31.442913    28.805632    34.080194\n",
       "2018-04-02 01:00:00     0  31.390724    28.753443    34.028005"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Backtesting with prediction intervals in test data using out-sample residuals\n",
    "# ==============================================================================\n",
    "cv = TimeSeriesFold(\n",
    "         steps              = 24, \n",
    "         initial_train_size = len(data.loc[:end_calibration]),\n",
    "         refit              = False\n",
    "     )\n",
    "\n",
    "metric, predictions = backtesting_forecaster(\n",
    "                          forecaster              = forecaster,\n",
    "                          y                       = data['OT'],\n",
    "                          exog                    = data[exog_features],\n",
    "                          cv                      = cv,\n",
    "                          metric                  = 'mean_absolute_error',\n",
    "                          interval                = [10, 90],  # 80% prediction interval\n",
    "                          interval_method         = 'conformal',\n",
    "                          use_in_sample_residuals = False,  # Use out-sample residuals\n",
    "                          use_binned_residuals    = True    # Adaptive conformal \n",
    "                      )\n",
    "predictions.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "287c82ac",
   "metadata": {},
   "source": [
    "<div class=\"admonition note\" name=\"html-admonition\" style=\"background: rgba(0,184,212,.1); padding-top: 0px; padding-bottom: 6px; border-radius: 8px; border-left: 8px solid #00b8d4; border-color: #00b8d4; padding-left: 10px; padding-right: 10px;\">\n",
    "\n",
    "<p class=\"title\">\n",
    "    <i style=\"font-size: 18px; color:#00b8d4;\"></i>\n",
    "    <b style=\"color: #00b8d4;\">&#9998 Note</b>\n",
    "</p>\n",
    "\n",
    "Two arguments control the use of residuals in `predict()` and `backtesting_forecaster()`:\n",
    "\n",
    "+ `use_in_sample_residuals`: If `True`, the in-sample residuals are used to compute the prediction intervals. Since these residuals are obtained from the training set, they are always available, but usually lead to overoptimistic intervals. If `False`, the out-sample residuals (calibration) are used to calculate the prediction intervals. These residuals are obtained from the validation/calibration set and are only available if the `set_out_sample_residuals()` method has been called. It is recommended to use out-sample residuals to achieve the desired coverage.\n",
    "\n",
    "+ `use_binned_residuals`: If `False`, a single correction factor is applied to all predictions during conformalization. If `True`, the conformalization process uses a correction factor that depends on the bin where the prediction falls. This can be thought as a type of <b>Adaptive Conformal Predictions</b> and it can lead to more accurate prediction intervals since the correction factor is conditioned on the region of the prediction space.\n",
    "\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "2bf240a9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot intervals\n",
    "# ==============================================================================\n",
    "plot_prediction_intervals(\n",
    "    predictions         = predictions,\n",
    "    y_true              = data_test,\n",
    "    target_variable     = \"OT\",\n",
    "    title               = \"Predicted intervals\",\n",
    "    kwargs_fill_between = {'color': 'gray', 'alpha': 0.4, 'zorder': 1}\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "3eae0ca6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 700x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot intervals with zoom ['2018-05-01', '2018-05-15']\n",
    "# ==============================================================================\n",
    "plot_prediction_intervals(\n",
    "    predictions         = predictions,\n",
    "    y_true              = data_test,\n",
    "    target_variable     = \"OT\",\n",
    "    initial_x_zoom      = ['2018-05-01', '2018-05-15'],\n",
    "    title               = \"Predicted intervals\",\n",
    "    kwargs_fill_between = {'color': 'gray', 'alpha': 0.4, 'zorder': 1}\n",
    ");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "67432fff",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted interval coverage: 79.32 %\n",
      "Area of the interval: 38409.35\n"
     ]
    }
   ],
   "source": [
    "# Predicted interval coverage (on test data)\n",
    "# ==============================================================================\n",
    "coverage = calculate_coverage(\n",
    "               y_true      = data.loc[end_calibration:, 'OT'],\n",
    "               lower_bound = predictions[\"lower_bound\"], \n",
    "               upper_bound = predictions[\"upper_bound\"]\n",
    "           )\n",
    "print(f\"Predicted interval coverage: {round(100 * coverage, 2)} %\")\n",
    "\n",
    "# Area of the interval\n",
    "# ==============================================================================\n",
    "area = (predictions[\"upper_bound\"] - predictions[\"lower_bound\"]).sum()\n",
    "print(f\"Area of the interval: {round(area, 2)}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d935daa6",
   "metadata": {},
   "source": [
    "The prediction intervals generated using conformal prediction achieve an empirical coverage very close to the nominal coverage of 80%."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ba462e64",
   "metadata": {},
   "source": [
    "<div class=\"admonition note\" name=\"html-admonition\" style=\"background: rgba(255,145,0,.1); padding-top: 0px; padding-bottom: 6px; border-radius: 8px; border-left: 8px solid #ff9100; border-color: #ff9100; padding-left: 10px; padding-right: 10px\">\n",
    "\n",
    "<p class=\"title\">\n",
    "    <i style=\"font-size: 18px; color:#ff9100; border-color: #ff9100;\"></i>\n",
    "    <b style=\"color: #ff9100;\"> <span style=\"color: #ff9100;\">&#9888;</span> Warning</b>\n",
    "</p>\n",
    "\n",
    "<p>\n",
    "<b>Probabilistic forecasting in production</b>\n",
    "\n",
    "The correct estimation of prediction intervals with conformal methods depends on the residuals being representative of future errors. For this reason, calibration residuals should be used. However, the dynamics of the series and models can change over time, so it is important to monitor and regularly update the residuals. It can be done easily using the <code>set_out_sample_residuals()</code> method.\n",
    "</p>\n",
    "\n",
    "</div>"
   ]
  }
 ],
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